Matrix Geometric Solutions

  • Randolph Nelson


In Example 8.12 we analyzed a scalar state process that was a modification of the M/M/1 queue. In the example, we classified two sets of states: boundary states and repeating states. Transitions between the repeating states had the property that rates from states 2, 3,..., were congruent to rates between states j, j + 1,... for all j ≥ 2. We noted in the example that this implied that the stationary distribution for the repeating portion of the process satisfied a geometric form. In this chapter we generalize this result to vector state processes that also have a repetitive structure. The technique we develop in this chapter to solve for the stationary state probabilities for such vector state Markov processes is called the matrix geometric method. (The theory of matrix geometric solutions was pioneered by Marcel Neuts; see [86] for a full development of the theory.) In much the same way that the repetition of the state transitions for this variation of the M/M/1 queue considered in Example 8.12 implied a geometric solution (with modifications made to account for boundary states), the repetition of the state transitions for vector processes implies a geometric form where scalars are replaced by matrices. We term such Markov processes matrix geometric processes.


Markov Process Phase Distribution Rate Matrix State Transition Diagram Main Processor 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliographic Notes

  1. [86]
    M.F. Neuts. Matrix Geometric Solutions in Stochastic Models. John Hopkins University Press, 1981.Google Scholar
  2. [88]
    M.F. Neuts. Structure of Stochastic Matrices of M/G/1 Type and Their Applications. Marcel-Deckker, 1990.Google Scholar
  3. [79]
    L. Lipsky. Queueing Theory - A Linear Algebra Approach. Macmillan, 1992.Google Scholar
  4. [114]
    H.C. Tijms. Stochastic Modelling and Analysis: A Computational Approach. John Wiley and Sons, 1986.Google Scholar
  5. [74]
    G. Latouche and V. Ramaswami. A logarithmic reduction algorithm for quasi-birth-and-death processes. Journal.of Applied Probability, 30: 650–674, 1993.MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Randolph Nelson
    • 1
    • 2
  1. 1.OTA Limited PartnershipPurchaseUSA
  2. 2.Modeling MethodologyIBM T.J. Watson Research CenterYorktown HeightsUSA

Personalised recommendations